Nr3D. The Nr3D dataset [1]} annotates the indoor 3D scene dataset ScanNet [2]} with \(45,503\)  human utterances. There are a total of 707 unique indoor scenes with target objects from 76 fine-grained classes. Each scene contains no more than six distractors (objects in the same class as the target).
Two kinds of data splits are used in Referit3D. The “easy” and “hard” splits depend on whether the scene contains more than two distractors. The “view-dep.” and “view-indep.” splits depend on whether the referring expression is dependent on the speaker's view or not.
